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State-space search

State-space search is a process used in the field of computer science, including artificial intelligence (AI), in which successive configurations or states of an instance are considered, with the intention of finding a goal state with the desired property.

Representation
In state-space search, a state space is formally represented as a tuple S: \langle S, A, \operatorname{Action}(s), \operatorname{Result}(s,a), \operatorname{Cost}(s,a) \rangle , in which: • S is the set of all possible states; • A is the set of possible actions, not related to a particular state but regarding all the state space; • \operatorname{Action}(s) is the function that establishes which action is possible to perform in a certain state; • \operatorname{Result}(s,a) is the function that returns the state reached performing action a in state s; • \operatorname{Cost}(s,a) is the cost of performing an action a in state s. In many state spaces, a is a constant, but this is not always true. == Examples of state-space search algorithms==
Examples of state-space search algorithms
Uninformed search According to Poole and Mackworth, the following are uninformed state-space search methods, meaning that they do not have any prior information about the goal's location. • Traditional depth-first searchBreadth-first searchIterative deepeningLowest-cost-first search / Uniform-cost search (UCS) Informed search These methods take the goal's location in the form of a heuristic function. Poole and Mackworth cite the following examples as informed search algorithms: • Informed/Heuristic depth-first search • Greedy best-first searchA* search ==See also==
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